SLICE_3

library(Seurat)
library(ggplot2)
library(cowplot)
library(dplyr)
library(devtools)

SLICE_3 <- Load10X_Spatial(data.dir = "/local1/DATA/sequencing/20200225_P1168_ASHLEY_10X_VISIUM_Lineage_Tracing_Visium_d15/processed/manual_alignments/SLICE_3/outs/", slice = "SLICE_3", filter.matrix = TRUE)

sctransfrom to normalize the data and detect high-variance features

SLICE_3 <- SCTransform(SLICE_3, assay = "Spatial", verbose = FALSE)

write table with raw data counts

write.table(SLICE_3@assays$SCT@counts, file = "~/cell_lineage/Visium/V19S23-053/SLICE_3/data_out/rawdataSLICE3_SCT.txt", quote = FALSE, sep = "\t")
write.table(SLICE_3@assays$Spatial@counts, file = "~/cell_lineage/Visium/V19S23-053/SLICE_3/data_out/rawdataSLICE3_Spatial.txt", quote = FALSE, sep = "\t")

SCT_cibsortx_input <- read.table("~/cell_lineage/Visium/V19S23-053/SLICE_3/data_out/rawdataSLICE3_SCT.txt", row.names = NULL, sep = "\t")
head(SCT_cibsortx_input)

names(SCT_cibsortx_input)[names(SCT_cibsortx_input) == "row.names"] <- "genes"
head(SCT_cibsortx_input)

write.table(SCT_cibsortx_input, file = "~/cell_lineage/Visium/V19S23-053/SLICE_3/data_out/SCT_cibsortx_input_SLICE_3.txt", quote = FALSE, sep = "\t", row.names=F)

Spatial_cibsortx_input <- read.table("~/cell_lineage/Visium/V19S23-053/SLICE_3/data_out/rawdataSLICE3_Spatial.txt", row.names = NULL, sep = "\t")
head(Spatial_cibsortx_input)

names(Spatial_cibsortx_input)[names(Spatial_cibsortx_input) == "row.names"] <- "genes"
head(Spatial_cibsortx_input)

write.table(Spatial_cibsortx_input, file = "~/cell_lineage/Visium/V19S23-053/SLICE_3/data_out/Spatial_cibsortx_input_SLICE_3.txt", quote = FALSE, sep = "\t", row.names=F)

interactive plot to identify spots not connected to tissue

ISpatialDimPlot(SLICE_3, image = NULL, alpha = c(0.3, 1))

Filter out spots with less than 1000 feature and not connected to tissue

# hist(SLICE_3$nFeature_Spatial)
# low_feature_cut <- 1000
# WhichCells(SLICE_3, expression = nFeature_Spatial < low_feature_cut)
# SpatialDimPlot(SLICE_3, cells.highlight = WhichCells(SLICE_3, expression = nFeature_Spatial < low_feature_cut))
# SpatialFeaturePlot(SLICE_3, features = 'nFeature_SCT')

# cells_drop <- WhichCells(SLICE_3, expression = nFeature_Spatial < low_feature_cut)
# all_cells_todrop <- rownames(SLICE_3@meta.data)[which(rownames(SLICE_3@meta.data) %in% c(cells_drop))] #all spots are touching tissue (this slice is manually tissue annotated upstream in loupe browser)
# 
# png("~/cell_lineage/Visium/V19S23-053/SLICE_3/plot_out/Dimplot_cellstodrop.png", width = 10, height = 10, units = "in", res = 300)
# SpatialDimPlot(SLICE_3, cells.highlight = all_cells_todrop, pt.size.factor = 2, stroke = .5)
# dev.off()
# 
# cells_to_keep <- rownames(SLICE_3@meta.data)[!rownames(SLICE_3@meta.data) %in% all_cells_todrop]
# 
# SLICE_3_sub <- subset(SLICE_3, cells = cells_to_keep)

SLICE_3 <- FindVariableFeatures(SLICE_3, selection.method = "vst", nfeatures = 2000)

Dimensionality reduction, clustering, and visualization

Find

dims = 1:9
SLICE_3 <- FindNeighbors(SLICE_3, reduction = "pca", dims = dims)

Computing nearest neighbor graph
Computing SNN

Find best resolution for clustering

Find clusters and run UMAP

res <- 0.5
SLICE_3 <- FindClusters(SLICE_3, verbose = FALSE, resolution = res)
SLICE_3 <- RunUMAP(SLICE_3, reduction = "pca", dims = dims)

09:50:08 UMAP embedding parameters a = 0.9922 b = 1.112
09:50:08 Read 587 rows and found 9 numeric columns
09:50:08 Using Annoy for neighbor search, n_neighbors = 30
09:50:08 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:50:09 Writing NN index file to temp file /tmp/RtmpmdRg7b/file1582d4c8daa07
09:50:09 Searching Annoy index using 1 thread, search_k = 3000
09:50:09 Annoy recall = 100%
09:50:09 Commencing smooth kNN distance calibration using 1 thread
09:50:10 Initializing from normalized Laplacian + noise
09:50:10 Commencing optimization for 500 epochs, with 21068 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:50:13 Optimization finished

plot clusters

plot nfeatures and ncounts

plot3 <- VlnPlot(SLICE_3, features = "nCount_SCT", pt.size = 0.1) + NoLegend()
plot4 <- SpatialFeaturePlot(SLICE_3, features = "nCount_SCT", pt.size.factor = 2.5, stroke = 0.5) + theme(legend.position = "right")
p3_p4 <- plot_grid(plot3, plot4)


plot5 <- VlnPlot(SLICE_3, features = "nFeature_SCT", pt.size = 0.1) + NoLegend()
plot6 <- SpatialFeaturePlot(SLICE_3, features = "nFeature_SCT", pt.size.factor = 2.5, stroke = 0.5) + theme(legend.position = "right")
p5_p6 <- plot_grid(plot5, plot6)

p3_p4

p5_p6

ggsave(p3_p4, filename = "~/cell_lineage/Visium/V19S23-053/SLICE_3/plot_out/nCountSCT.png", height = 10, width = 20, units = "in", dpi = 300)

ggsave(p5_p6, filename = "~/cell_lineage/Visium/V19S23-053/SLICE_3/plot_out/nFeatureSCT.png", height = 10, width = 20, units = "in", dpi = 300)

p7_p8
p9_p10


ggsave(p7_p8, filename = "~/cell_lineage/Visium/V19S23-053/SLICE_3/plot_out/clusters_nCountSCT.pdf", height = 7, width = 16, units = "in")
ggsave(p9_p10, filename = "~/cell_lineage/Visium/V19S23-053/SLICE_3/plot_out/clusters_nFeaturesSCT.pdf", height = 7, width = 16, units = "in")

Highlight cells

SpatialDimPlot(SLICE_3, cells.highlight = CellsByIdentities(object = SLICE_3, idents = c(0, 1, 2, 3, 4, 5, 6)), facet.highlight = TRUE, ncol = 3, pt.size.factor = 2.5)

Calculate NPC and Neuron Scores

Voxhunt

pdf(file= "~/cell_lineage/Visium/V19S23-053/SLICE_3/plot_out/voxhunt.pdf")
p <- plot_map(vox_map)

adding dummy grobs

p
dev.off()

null device 
          1 

p

Feature Plots

SpatialFeaturePlot(SLICE_3, features = c("Tomato","OTX2", 'PAX6'), ncol = 3, pt.size.factor = 2.5)
SpatialFeaturePlot(SLICE_3, features = c("TBR1", "NEUROD6", "FOXG1"), ncol = 3, pt.size.factor = 2.5)

SpatialFeaturePlot(SLICE_3, features = c("HOXB2", "HOXB8", "HOXB5"), ncol = 3, pt.size.factor = 2.5)

SpatialFeaturePlot(SLICE_3, features = c("DCN", "TTR", "PRPH"), ncol = 3, pt.size.factor = 2.5)

SpatialFeaturePlot(SLICE_3, features = c("NEUROD1", "SOX10", "PMEL"), ncol = 3, pt.size.factor = 2.5)

SpatialFeaturePlot(SLICE_3, features = c("RSPO2", "RSPO3", "MEIS1"), ncol = 3, pt.size.factor = 2.5)

SLICE_3_coords <- SLICE_3@images$SLICE_3@coordinates
SLICE_3_anno <- cbind(SLICE_3@meta.data, SLICE_3_coords)
write.csv(SLICE_3_anno, file = "~/cell_lineage/Visium/V19S23-053/SLICE_3/data_out/SLICE_3_anno_w_coords.csv")